结合感知、情境和NREM睡眠介导的噪声复原力的渐进式学习的丘脑-皮层发放模型
Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep-mediated noise-resilience
摘要：
大脑表现出从少数嘈杂的例子中快速增量学习的能力，以及将相似的记忆关联到精心创建的类别中并将上下文提示与感官知觉结合起来的能力。与睡眠一起，这些机制被认为是许多高级认知功能的关键组成部分。然而，人们对不同大脑状态的潜在过程和具体作用知之甚少。在这项工作中，我们利用了结合的上下文和感知的丘脑-皮层模型的基础上，一个软赢家通吃电路的兴奋性和抑制性尖峰神经元。在校准这个模型来表达清醒和深度睡眠状态的功能与生物措施相媲美后，我们证明了模型的快速增量学习能力，从几个例子中，它的弹性时，提出了嘈杂的看法和上下文信号，并改善视觉分类后，睡眠由于诱导突触稳态和类似的记忆。
英文摘要：
The brain exhibits capabilities of fast incremental learning from few noisy examples, as well as the ability to associate similar memories in autonomously-created categories and to combine contextual hints with sensory perceptions. Together with sleep, these mechanisms are thought to be key components of many high-level cognitive functions. Yet, little is known about the underlying processes and the specific roles of different brain states. In this work, we exploited the combination of context and perception in a thalamo-cortical model based on a soft winner-take-all circuit of excitatory and inhibitory spiking neurons. After calibrating this model to express awake and deep-sleep states with features comparable with biological measures, we demonstrate the model capability of fast incremental learning from few examples, its resilience when proposed with noisy perceptions and contextual signals, and an improvement in visual classification after sleep due to induced synaptic homeostasis and association of similar memories.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
